A Serum Metabolomic Signature for the Detection and Grading of Bladder Cancer

Bladder cancer has a high incidence and is marked by high morbidity and mortality. Early diagnosis is still challenging. The objective of this study was to create a metabolomics-based profile of bladder cancer in order to provide a novel approach for disease screening and stratification. Moreover, the study characterized the metabolic changes associated with the disease. Serum metabolomic profiles were obtained from 149 bladder cancer patients and 81 healthy controls. Different ensemble machine learning models were built in order to: (1) differentiate cancer patients from controls; (2) stratify cancer patients according to grading; (3) stratify patients according to cancer muscle invasiveness. Ensemble machine learning models were able to discriminate well between cancer patients and controls, between high grade (G3) and low grade (G1-2) cancers and between different degrees of muscle invasivity; ensemble model accuracies were ≥80%. Relevant metabolites, selected using the partial least square discriminant analysis (PLS-DA) algorithm, were included in a metabolite-set enrichment analysis, showing perturbations primarily associated with cell glucose metabolism. The metabolomic approach may be useful as a non-invasive screening tool for bladder cancer. Furthermore, metabolic pathway analysis can increase understanding of cancer pathophysiology. Studies conducted on larger cohorts, and including blind trials, are needed to validate results.

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Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

Remote Sensing ◽

10.3390/rs13040641 ◽

2021 ◽

Vol 13 (4) ◽

pp. 641

Author(s):

Gopal Ramdas Mahajan ◽

Bappa Das ◽

Dayesh Murgaokar ◽

Ittai Herrmann ◽

Katja Berger ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Support Vector ◽

Spectral Indices ◽

Learning Models ◽

Leaf Nutrients ◽

Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

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Machine learning models for predicting post-cystectomy recurrence and survival in bladder cancer patients

PLoS ONE ◽

10.1371/journal.pone.0210976 ◽

2019 ◽

Vol 14 (2) ◽

pp. e0210976 ◽

Cited By ~ 10

Author(s):

Zaki Hasnain ◽

Jeremy Mason ◽

Karanvir Gill ◽

Gus Miranda ◽

Inderbir S. Gill ◽

...

Keyword(s):

Machine Learning ◽

Bladder Cancer ◽

Cancer Patients ◽

Learning Models ◽

Machine Learning Models

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Ensemble machine learning models for aviation incident risk prediction

Decision Support Systems ◽

10.1016/j.dss.2018.10.009 ◽

2019 ◽

Vol 116 ◽

pp. 48-63 ◽

Cited By ~ 21

Author(s):

Xiaoge Zhang ◽

Sankaran Mahadevan

Keyword(s):

Machine Learning ◽

Risk Prediction ◽

Learning Models ◽

Ensemble Machine Learning ◽

Machine Learning Models

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Data-Driven Approach for Predicting and Explaining the Risk of Long-Term Unemployment

E3S Web of Conferences ◽

10.1051/e3sconf/202021401023 ◽

2020 ◽

Vol 214 ◽

pp. 01023

Author(s):

Linan (Frank) Zhao

Keyword(s):

Machine Learning ◽

Age Groups ◽

Learning Models ◽

Public Authorities ◽

Ensemble Machine Learning ◽

European Public ◽

Data Driven Approach ◽

Using Data ◽

Machine Learning Models

Long-term unemployment has significant societal impact and is of particular concerns for policymakers with regard to economic growth and public finances. This paper constructs advanced ensemble machine learning models to predict citizens’ risks of becoming long-term unemployed using data collected from European public authorities for employment service. The proposed model achieves 81.2% accuracy on identifying citizens with high risks of long-term unemployment. This paper also examines how to dissect black-box machine learning models by offering explanations at both a local and global level using SHAP, a state-of-the-art model-agnostic approach to explain factors that contribute to long-term unemployment. Lastly, this paper addresses an under-explored question when applying machine learning in the public domain, that is, the inherent bias in model predictions. The results show that popular models such as gradient boosted trees may produce unfair predictions against senior age groups and immigrants. Overall, this paper sheds light on the recent increasing shift for governments to adopt machine learning models to profile and prioritize employment resources to reduce the detrimental effects of long-term unemployment and improve public welfare.

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Efficient Breast Cancer Prediction Using Ensemble Machine Learning Models

2019 4th International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) ◽

10.1109/rteict46194.2019.9016968 ◽

2019 ◽

Cited By ~ 1

Author(s):

Naveen ◽

R. K. Sharma ◽

Anil Ramachandran Nair

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Learning Models ◽

Cancer Prediction ◽

Ensemble Machine Learning ◽

Machine Learning Models

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Machine Learning Models That Integrate Tumor Texture and Perfusion Characteristics Using Low-Dose Breast Computed Tomography Are Promising for Predicting Histological Biomarkers and Treatment Failure in Breast Cancer Patients

Cancers ◽

10.3390/cancers13236013 ◽

2021 ◽

Vol 13 (23) ◽

pp. 6013

Author(s):

Hyun-Soo Park ◽

Kwang-sig Lee ◽

Bo-Kyoung Seo ◽

Eun-Sil Kim ◽

Kyu-Ran Cho ◽

...

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Random Forest ◽

Cancer Patients ◽

Low Dose ◽

Random Forest Model ◽

Breast Cancer Patients ◽

Learning Models ◽

Forest Model ◽

Machine Learning Models

This prospective study enrolled 147 women with invasive breast cancer who underwent low-dose breast CT (80 kVp, 25 mAs, 1.01–1.38 mSv) before treatment. From each tumor, we extracted eight perfusion parameters using the maximum slope algorithm and 36 texture parameters using the filtered histogram technique. Relationships between CT parameters and histological factors were analyzed using five machine learning algorithms. Performance was compared using the area under the receiver-operating characteristic curve (AUC) with the DeLong test. The AUCs of the machine learning models increased when using both features instead of the perfusion or texture features alone. The random forest model that integrated texture and perfusion features was the best model for prediction (AUC = 0.76). In the integrated random forest model, the AUCs for predicting human epidermal growth factor receptor 2 positivity, estrogen receptor positivity, progesterone receptor positivity, ki67 positivity, high tumor grade, and molecular subtype were 0.86, 0.76, 0.69, 0.65, 0.75, and 0.79, respectively. Entropy of pre- and postcontrast images and perfusion, time to peak, and peak enhancement intensity of hot spots are the five most important CT parameters for prediction. In conclusion, machine learning using texture and perfusion characteristics of breast cancer with low-dose CT has potential value for predicting prognostic factors and risk stratification in breast cancer patients.

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